Retrieval of forest leaf functional traits from HySpex imagery using radiative transfer models and continuous wavelet analysis

Our results revealed strong correlations between six wavelet features and LDMC, as well as between four wavelet features and SLA. The wavelet features at 1741 nm (scale 5) and 2281 nm (scale 4) were the two most strongly correlated with LDMC and SLA respectively. The combination of all the identified wavelet features for LDMC yielded the most accurate prediction (R2 = 0.59 and RMSE = 4.39%). However, for SLA the most accurate prediction was obtained from the single most correlated feature: 2281 nm, scale 4 (R2 = 0.85 and RMSE = 4.90). Our results demonstrate the applicability of Continuous Wavelet Analysis (CWA) when inverting radiative transfer models, for accurate mapping of forest leaf functional traits.